code: def _export_to_json(json_name, xs, xlabel, ys, ylabel, ys_std):
      """Save selected csv column to JSON preparing for automatic benchmarking.
  
      Args:
          json_name (str): The JSON file name.
          xs (list): List of x axis points
          xlabel (str): Label name for x axis.
          ys (np.array): List of y axis points
          ylabel (str): Label name for y axis.
          ys_std (np.array): Standard deviation of y asis, used to calculate
              upper and lower boundary for confidence interval.
  
      """
      json_path = os.path.join(_log_dir, "auto", json_name + ".json")
      with open(json_path, "w") as json_file:
          json.dump(
              dict(
                  x=xs,
                  y=ys.tolist(),
                  y_min=(ys - ys_std).tolist(),
                  y_max=(ys + ys_std).tolist(),
                  xlabel=xlabel,
                  ylabel=ylabel,
                  git_hash=_get_git_hash(),
              ),
              json_file,
          )
  
file_path: garage/benchmarks/src/garage_benchmarks/helper.py
imports: import csv
  import functools
  import json
  import os
  import pathlib
  import random
  import subprocess
  from google.cloud import storage
  import matplotlib.pyplot
  import numpy
  import tensorflow
label_desc: Use image node_id 5 for calling a global function with example usage: _export_to_json(json_name, xs, xlabel, ys, ylabel, ys_std) without return types